------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\mbeissin\Desktop\Stata files for book\Logfiles\chapter6.log
  log type:  text
 opened on:  25 Jan 2022, 22:13:53

. * ============================================================================
. * STATISTICAL RESULTS APPEARING IN CHAPTER 6
. * STATA Do file for Chapter 6  
. * Results reported in Chapter 6  
. * Author: Mark R. Beissinger  
. * Date:  January 2022  
. * Princeton, NJ 
. * =============================================================================
. * BEFORE RUNNING, YOU MUST SET THE DEFAULT PATH FOR WHERE THE DATA
. *   FILES RESIDE
. * =============================================================================
. * Before running, you must download the following packages for STATA:
. *   firthlogit from http://fmwww.bc.edu/RePEc/bocode/f
. * =============================================================================
. * The following datafiles are used for this chapter:
. *   Data on revolutionary episodes--revolutionaryeps.dta
. *   Data set of urban civic revolt locations--urbancivicspaces.dta
. *   Data set on population of capital cities, 1900-2014--capitalpopulations.dta
. * =============================================================================
. * Output produced:  Logfiles\chapter6.log
. *                                       Logfiles\figure6_1.pdf
. *   --All output from this chapter has been combined into a single file and can 
. *               be found in a pdf file (chapter6.pdf) in the "Outputfiles" folder
. * =============================================================================
. 
. * =========================================================
. * DEMONSTRATIONS VS. OTHER TACTICAL FORMS IN URBAN REVOLTS
. * =========================================================
. clear

. use revolutionaryeps.dta

. logit success strikes demonstrations riots armed startyear if urbandum==1, or

Iteration 0:   log likelihood = -124.05444  
Iteration 1:   log likelihood = -109.46646  
Iteration 2:   log likelihood = -109.28227  
Iteration 3:   log likelihood = -109.28146  
Iteration 4:   log likelihood = -109.28146  

Logistic regression                             Number of obs     =        180
                                                LR chi2(5)        =      29.55
                                                Prob > chi2       =     0.0000
Log likelihood = -109.28146                     Pseudo R2         =     0.1191

--------------------------------------------------------------------------------
       success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       strikes |   1.752496   .6425338     1.53   0.126      .854227    3.595348
demonstrations |   8.571434   4.612093     3.99   0.000     2.985667    24.60739
         riots |   .7043604   .2351419    -1.05   0.294     .3661272    1.355058
         armed |   1.367888    .509012     0.84   0.400     .6596373    2.836584
     startyear |     1.0026   .0061249     0.43   0.671     .9906668    1.014676
         _cons |   .0007011   .0084596    -0.60   0.547     3.76e-14    1.31e+07
--------------------------------------------------------------------------------

. margins, atmeans at(strikes=0 demonstration=1 riots=0 armed=0)

Adjusted predictions                            Number of obs     =        180
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : strikes         =           0
               demonstrat~s    =           1
               riots           =           0
               armed           =           0
               startyear       =    1968.472 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .499103   .0869134     5.74   0.000     .3287558    .6694502
------------------------------------------------------------------------------

. margins, atmeans at(strikes=1 demonstration=0 riots=0 armed=0)

Adjusted predictions                            Number of obs     =        180
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : strikes         =           1
               demonstrat~s    =           0
               riots           =           0
               armed           =           0
               startyear       =    1968.472 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1692458   .0865073     1.96   0.050    -.0003054     .338797
------------------------------------------------------------------------------

. margins, atmeans at(strikes=1 demonstration=0 riots=1 armed=1)

Adjusted predictions                            Number of obs     =        180
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : strikes         =           1
               demonstrat~s    =           0
               riots           =           1
               armed           =           1
               startyear       =    1968.472 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1640799   .0842534     1.95   0.051    -.0010537    .3292135
------------------------------------------------------------------------------

. * Controlling for participation
. logit success strikes demonstrations riots  armed startyear lnparticnum if urbandum==1, or

Iteration 0:   log likelihood = -109.07246  
Iteration 1:   log likelihood = -97.176813  
Iteration 2:   log likelihood = -97.040146  
Iteration 3:   log likelihood = -97.039769  
Iteration 4:   log likelihood = -97.039769  

Logistic regression                             Number of obs     =        159
                                                LR chi2(6)        =      24.07
                                                Prob > chi2       =     0.0005
Log likelihood = -97.039769                     Pseudo R2         =     0.1103

--------------------------------------------------------------------------------
       success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       strikes |   1.600053   .6252677     1.20   0.229     .7438791    3.441648
demonstrations |   3.537497   2.057164     2.17   0.030     1.131608     11.0585
         riots |   .7414276   .2646736    -0.84   0.402     .3683055    1.492551
         armed |   1.340376   .5520802     0.71   0.477     .5979014    3.004854
     startyear |   1.008539   .0068384     1.25   0.210     .9952247    1.022031
   lnparticnum |    1.28457   .1413566     2.28   0.023     1.035357     1.59377
         _cons |   6.69e-10   9.05e-09    -1.56   0.118     2.03e-21    220.5788
--------------------------------------------------------------------------------

. * In rural revolutions
. logit success demonstrations riots landseizures c.startyear##c.startyear if urbandum==0, or

Iteration 0:   log likelihood = -92.509634  
Iteration 1:   log likelihood = -86.935366  
Iteration 2:   log likelihood = -86.618454  
Iteration 3:   log likelihood = -86.617122  
Iteration 4:   log likelihood = -86.617122  

Logistic regression                             Number of obs     =        165
                                                LR chi2(5)        =      11.79
                                                Prob > chi2       =     0.0379
Log likelihood = -86.617122                     Pseudo R2         =     0.0637

-----------------------------------------------------------------------------------------
                success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
         demonstrations |   2.057846   1.341908     1.11   0.268     .5732577    7.387135
                  riots |   1.835451   1.738874     0.64   0.522     .2866351    11.75321
           landseizures |   1.611877   1.190476     0.65   0.518     .3790216    6.854879
              startyear |   5.800117   4.888309     2.09   0.037     1.111856    30.25695
                        |
c.startyear#c.startyear |   .9995542    .000215    -2.07   0.038      .999133    .9999756
                        |
                  _cons |          0          0    -2.10   0.036            0    8.34e-51
-----------------------------------------------------------------------------------------

. 
. * =======================================================================
. * ROLE OF LAND SEIZURES IN RURAL REVOLUTIONS IN MAGNIFYING PARTICIPATION
. * =======================================================================
. glm particnum landseizures lndeaths startyear if startyear>1899 & urbandum==0, family(gamma) link(log) vce(robus
> t)

Iteration 0:   log pseudolikelihood = -1692.9528  
Iteration 1:   log pseudolikelihood =  -1676.902  
Iteration 2:   log pseudolikelihood = -1675.6345  
Iteration 3:   log pseudolikelihood = -1675.6258  
Iteration 4:   log pseudolikelihood = -1675.6258  

Generalized linear models                         No. of obs      =        148
Optimization     : ML                             Residual df     =        144
                                                  Scale parameter =    2.02605
Deviance         =   183.622069                   (1/df) Deviance =   1.275153
Pearson          =  291.7511503                   (1/df) Pearson  =    2.02605

Variance function: V(u) = u^2                     [Gamma]
Link function    : g(u) = ln(u)                   [Log]

                                                  AIC             =   22.69765
Log pseudolikelihood = -1675.625817               BIC             =  -535.9765

------------------------------------------------------------------------------
             |               Robust
   particnum |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
landseizures |      1.633   .6111235     2.67   0.008     .4352199     2.83078
    lndeaths |   .3163745   .0529149     5.98   0.000     .2126632    .4200859
   startyear |  -.0098668   .0032872    -3.00   0.003    -.0163095   -.0034241
       _cons |   26.52084   6.525615     4.06   0.000     13.73087    39.31081
------------------------------------------------------------------------------

. margins, atmeans at(landseizures=(0 1))

Adjusted predictions                            Number of obs     =        148
Model VCE    : Robust

Expression   : Predicted mean particnum, predict()

1._at        : landseizures    =           0
               lndeaths        =    9.646049 (mean)
               startyear       =     1960.02 (mean)

2._at        : landseizures    =           1
               lndeaths        =    9.646049 (mean)
               startyear       =     1960.02 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   27820.43   3286.764     8.46   0.000     21378.49    34262.37
          2  |   142418.6   85108.04     1.67   0.094    -24390.11    309227.3
------------------------------------------------------------------------------

. 
. * ===============================================
. * ROLE OF SYMBOLIC SPACES IN URBAN CIVIC REVOLTS
. * ===============================================
. clear

. use urbancivicspaces.dta

. ttest particnum, by( natrelsymbols)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      17    214529.4    88461.53    364736.2    26999.35    402059.5
       1 |      37    553783.8    105696.9    642928.9    339420.6    768146.9
---------+--------------------------------------------------------------------
combined |      54    446981.5    80072.51    588410.3    286376.4    607586.6
---------+--------------------------------------------------------------------
    diff |           -339254.4    167576.9               -675522.1   -2986.637
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.0245
Ho: diff = 0                                     degrees of freedom =       52

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0240         Pr(|T| > |t|) = 0.0481          Pr(T > t) = 0.9760

. 
. * =======================================================================
. * RELATIONSHIP BETWEEN USE OF SQUARES AND SUCCESS IN URBAN CIVIC REVOLTS
. * =======================================================================
. tab rallysquare success, col chi

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

  Involved |
     major |  Opposition came to
rallies in |        power?
   squares |         0          1 |     Total
-----------+----------------------+----------
         0 |         4          9 |        13 
           |     20.00      26.47 |     24.07 
-----------+----------------------+----------
         1 |        16         25 |        41 
           |     80.00      73.53 |     75.93 
-----------+----------------------+----------
     Total |        20         34 |        54 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.2884   Pr = 0.591

. 
. * ==================================================
. * DISTANCE OF URBAN CIVIC REVOLTS FROM SEAT OF GOVT
. * ==================================================
. sum distance, detail

      Distance of coordinating point from seat of govt
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                  54
25%     .4661755              0       Sum of Wgt.          54

50%     1.978969                      Mean           17.36105
                        Largest       Std. Dev.      105.2786
75%     4.330577       13.33045
90%     9.807342        15.1268       Variance       11083.58
95%      15.1268       15.19437       Skewness        7.12759
99%       776.15         776.15       Kurtosis       51.87645

. 
. * ==========================================================================
. * FIGURE 6.1: DISTANCE OF URBAN CIVIC REVOLTS FROM SEAT OF GOVT, BY OUTCOME
. * ==========================================================================
. graph box newdistance, over(success)

. graph export Logfiles\figure6_1.pdf, replace
(file Logfiles\figure6_1.pdf written in PDF format)

. 
. * =====================================================
. * T-TEST OF EFFECT OF DISTANCE SEAT OF GOVT ON OUTCOME
. * =====================================================
. ttest newdistance , by(success)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      20    4.739959    1.261237    5.640422     2.10016    7.379758
       1 |      34    2.545511    .6135846    3.577782    1.297164    3.793858
---------+--------------------------------------------------------------------
combined |      54    3.358269    .6164332     4.52984    2.121861    4.594678
---------+--------------------------------------------------------------------
    diff |            2.194448    1.252284               -.3184434    4.707339
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.7524
Ho: diff = 0                                     degrees of freedom =       52

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9572         Pr(|T| > |t|) = 0.0856          Pr(T > t) = 0.0428

. 
. * ==========================================================================
. * PROBABILITY OF OPPOSITION VICTORY IF WITHIN 3/4 KILOMETER OF SEAT OF GOVT
. * ==========================================================================
. logit success veryclose lnpartic, or nolog

Logistic regression                             Number of obs     =         54
                                                LR chi2(2)        =      11.67
                                                Prob > chi2       =     0.0029
Log likelihood = -29.757033                     Pseudo R2         =     0.1640

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   veryclose |   4.529672   3.080137     2.22   0.026     1.194695    17.17419
    lnpartic |   1.916882   .4943577     2.52   0.012     1.156307    3.177734
       _cons |   .0003579   .0011398    -2.49   0.013     6.96e-07    .1840147
------------------------------------------------------------------------------

. margins, atmeans at(veryclose=(0 1))

Adjusted predictions                            Number of obs     =         54
Model VCE    : OIM

Expression   : Pr(success), predict()

1._at        : veryclose       =           0
               lnpartic        =    12.21851 (mean)

2._at        : veryclose       =           1
               lnpartic        =    12.21851 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5038073   .0982624     5.13   0.000     .3112165     .696398
          2  |   .8214025    .081024    10.14   0.000     .6625984    .9802066
------------------------------------------------------------------------------

. * Further controls
. firthlogit success veryclose lnpartic c.newpolitymin1##c.newpolitymin1 lnoill newmilexpsold10tile newgdppcthl ne
> wincumbpowerdur

initial:       penalized log likelihood = -11.536805
rescale:       penalized log likelihood = -11.536805
Iteration 0:   penalized log likelihood = -11.536805  
Iteration 1:   penalized log likelihood =  1.9229477  (not concave)
Iteration 2:   penalized log likelihood =  2.3615416  (not concave)
Iteration 3:   penalized log likelihood =   2.406643  (not concave)
Iteration 4:   penalized log likelihood =  2.4925638  
Iteration 5:   penalized log likelihood =  2.7562104  (not concave)
Iteration 6:   penalized log likelihood =  2.8270428  (not concave)
Iteration 7:   penalized log likelihood =  2.8396039  
Iteration 8:   penalized log likelihood =  2.8520085  (not concave)
Iteration 9:   penalized log likelihood =  2.9008369  (not concave)
Iteration 10:  penalized log likelihood =  2.9247795  (not concave)
Iteration 11:  penalized log likelihood =  2.9465353  
Iteration 12:  penalized log likelihood =   3.030048  (not concave)
Iteration 13:  penalized log likelihood =  3.0510674  (not concave)
Iteration 14:  penalized log likelihood =   3.056185  
Iteration 15:  penalized log likelihood =  3.2929021  (not concave)
Iteration 16:  penalized log likelihood =  3.2972363  
Iteration 17:  penalized log likelihood =  3.3282653  (not concave)
Iteration 18:  penalized log likelihood =  3.3363071  
Iteration 19:  penalized log likelihood =  3.3390151  (not concave)
Iteration 20:  penalized log likelihood =  3.3396419  
Iteration 21:  penalized log likelihood =  3.3494973  
Iteration 22:  penalized log likelihood =  3.3496247  
Iteration 23:  penalized log likelihood =  3.3496247  

                                                Number of obs     =         50
                                                Wald chi2(8)      =       9.00
Penalized log likelihood =  3.3496247           Prob > chi2       =     0.3427

-------------------------------------------------------------------------------------------------
                        success |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
                      veryclose |   2.778612   1.402945     1.98   0.048     .0288907    5.528332
                       lnpartic |   1.232513   .5301483     2.32   0.020     .1934416    2.271585
                  newpolitymin1 |  -.1364096   .0958273    -1.42   0.155    -.3242277    .0514085
                                |
c.newpolitymin1#c.newpolitymin1 |  -.0555974     .02915    -1.91   0.056    -.1127303    .0015355
                                |
                         lnoill |  -.3672495   .1682674    -2.18   0.029    -.6970475   -.0374515
            newmilexpsold10tile |   .5817355   .3031533     1.92   0.055    -.0124341    1.175905
                    newgdppcthl |  -.4893041   .2127537    -2.30   0.021    -.9062937   -.0723144
              newincumbpowerdur |   -.042447   .0495592    -0.86   0.392    -.1395812    .0546871
                          _cons |  -13.35179   5.846416    -2.28   0.022    -24.81056   -1.893031
-------------------------------------------------------------------------------------------------

. 
. * ====================================================
. * RELATIONSHIP OF PROTEST OUTSIDE CAPITAL AND SUCCESS
. * ====================================================
. tab keyinother success, col chi

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

Key events |
  occurred |  Opposition came to
  in other |        power?
    cities |         0          1 |     Total
-----------+----------------------+----------
         0 |        12          3 |        15 
           |     60.00       8.82 |     27.78 
-----------+----------------------+----------
         1 |         8         31 |        39 
           |     40.00      91.18 |     72.22 
-----------+----------------------+----------
     Total |        20         34 |        54 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =  16.4395   Pr = 0.000

. 
. * =========================================================================
. * DATA FOR FIGURE 6.2:  CONCENTRATION OF POPULATIONS AROUND CAPITAL CITIES
. * =========================================================================
. clear

. use capitalpopulations.dta

. table year , c( mean capperc semean capperc) replace

----------------------------------------
     Year | mean(capperc)   sem(capperc)
----------+-----------------------------
     1900 |       6.58712       .7957903
     1901 |       6.58273        .795761
     1902 |       6.61672       .7889279
     1903 |       6.65555       .7833344
     1904 |       6.69906       .7790454
     1905 |       6.73581       .7694811
     1906 |       6.87008       .7779392
     1907 |       6.86546        .760694
     1908 |       6.90338       .7578807
     1909 |       6.92231       .7522704
     1910 |        6.9682       .7369813
     1911 |        6.9635          .7498
     1912 |        7.0073       .7520484
     1913 |       7.05212         .75652
     1914 |       7.00036       .7386864
     1915 |       7.17102       .7708603
     1916 |       7.19336       .7660974
     1917 |       7.20912       .7601197
     1918 |       7.23766       .7577354
     1919 |        7.2181       .7381988
     1920 |       7.18377       .7269167
     1921 |        7.3083       .7233708
     1922 |       7.37929       .7198451
     1923 |       7.39317        .712715
     1924 |       7.39838       .7143752
     1925 |       7.45444       .7139815
     1926 |       7.30459       .6999279
     1927 |       7.33361       .6960205
     1928 |       7.30605       .6959132
     1929 |       7.38381       .6958028
     1930 |       7.46408        .696675
     1931 |       7.33263       .6828979
     1932 |       7.47066       .6923008
     1933 |       7.61954       .7030906
     1934 |       7.75026       .7129241
     1935 |       7.87454       .7243933
     1936 |       8.00408       .7335835
     1937 |       8.13875        .744998
     1938 |       8.20771       .7535436
     1939 |       8.31529       .7608243
     1940 |       8.43399       .7744224
     1941 |       8.56444       .7729207
     1942 |       8.72869        .786218
     1943 |       8.89216       .7987826
     1944 |       9.04098       .8112971
     1945 |       9.20791       .8250737
     1946 |       9.39915       .8371363
     1947 |       9.57699       .8491343
     1948 |       9.77549       .8665425
     1949 |       9.95869       .8771753
     1950 |       9.75781       .8620117
     1951 |       9.89388       .8606892
     1952 |       10.0456       .8659089
     1953 |       10.2032        .872366
     1954 |       10.3687       .8813104
     1955 |       10.5568       .8876024
     1956 |       10.7342       .8999038
     1957 |       10.9142       .9119786
     1958 |       11.1232       .9256628
     1959 |        11.309       .9337971
     1960 |       11.4845       .9416586
     1961 |       11.5922       .9405058
     1962 |       11.7067       .9292297
     1963 |       11.8256       .9213496
     1964 |       11.8886       .9115081
     1965 |       12.0371        .907215
     1966 |       12.2032       .9084904
     1967 |       12.3684       .9089029
     1968 |       12.5691       .9164597
     1969 |       12.7946       .9264078
     1970 |       12.8122       .9284249
     1971 |       13.0332       .9326064
     1972 |       13.2322       .9289494
     1973 |       13.4385       .9279646
     1974 |       13.6062       .9241056
     1975 |       13.6418       .9246249
     1976 |       13.8092       .9288763
     1977 |       13.9829       .9299579
     1978 |       14.1207       .9281571
     1979 |       14.2632       .9310257
     1980 |        14.418        .933912
     1981 |       14.5693       .9377643
     1982 |       14.6748       .9417764
     1983 |       14.6328       .9372209
     1984 |       14.7133       .9330426
     1985 |        14.811       .9307839
     1986 |       14.9052       .9270555
     1987 |       14.9839       .9214527
     1988 |       15.0808       .9189064
     1989 |       15.1671       .9194004
     1990 |       15.3737       .9361398
     1991 |       15.4795       .9662982
     1992 |       15.3935       .9314533
     1993 |       15.3784        .919266
     1994 |       15.3825       .9122294
     1995 |       15.3821       .9062214
     1996 |       15.5399       .9214403
     1997 |       15.5321       .9135283
     1998 |       15.5106       .9086018
     1999 |       15.5474       .9058896
     2000 |       15.6309       .9081395
     2001 |       15.7313       .9126087
     2002 |       15.8909       .9210472
     2003 |       15.9988       .9286486
     2004 |       16.0932       .9358383
     2005 |       16.0844       .9374236
     2006 |       16.1415        .937663
     2007 |       16.1612       .9414387
     2008 |       16.2468       .9468728
     2009 |       16.3935       .9629145
     2010 |       16.4698       .9660709
     2011 |       16.5673       .9757501
     2012 |       16.7482       .9961383
     2013 |       16.9165       1.005405
     2014 |          17.1       1.016938
----------------------------------------

. rename table1 mean

. rename table2 semean

. generate ul= mean + (1.96 * semean)

. generate ll= mean - (1.96 * semean)

. list year mean ul ll, clean noobs

    year      mean         ul         ll  
    1900   6.58712   8.146869   5.027371  
    1901   6.58273   8.142426   5.023043  
    1902   6.61672   8.163017    5.07042  
    1903   6.65555   8.190882   5.120211  
    1904   6.69906   8.225991   5.172133  
    1905   6.73581   8.243997   5.227631  
    1906   6.87008   8.394838   5.345316  
    1907   6.86546   8.356421   5.374501  
    1908   6.90338   8.388831   5.417938  
    1909   6.92231   8.396757   5.447857  
    1910    6.9682   8.412679   5.523712  
    1911    6.9635   8.433112   5.493896  
    1912    7.0073   8.481315   5.533285  
    1913   7.05212   8.534896   5.569338  
    1914   7.00036    8.44819   5.552539  
    1915   7.17102   8.681908   5.660136  
    1916   7.19336   8.694908   5.691807  
    1917   7.20912   8.698958   5.719289  
    1918   7.23766   8.722826   5.752503  
    1919    7.2181   8.664971   5.771232  
    1920   7.18377   8.608525   5.759011  
    1921    7.3083   8.726105   5.890491  
    1922   7.37929   8.790187   5.968394  
    1923   7.39317   8.790091   5.996247  
    1924   7.39838   8.798555   5.998205  
    1925   7.45444   8.853841   6.055033  
    1926   7.30459   8.676448   5.932731  
    1927   7.33361   8.697805   5.969405  
    1928   7.30605   8.670045   5.942065  
    1929   7.38381   8.747583   6.020036  
    1930   7.46408   8.829565   6.098599  
    1931   7.33263   8.671111   5.994152  
    1932   7.47066   8.827567   6.113748  
    1933   7.61954   8.997597   6.241482  
    1934   7.75026   9.147594   6.352932  
    1935   7.87454   9.294351   6.454729  
    1936   8.00408   9.441902   6.566255  
    1937   8.13875   9.598947   6.678554  
    1938   8.20771   9.684658   6.730767  
    1939   8.31529    9.80651   6.824079  
    1940   8.43399   9.951855   6.916119  
    1941   8.56444   10.07937    7.04952  
    1942   8.72869   10.26968   7.187706  
    1943   8.89216   10.45777   7.326543  
    1944   9.04098   10.63112   7.450838  
    1945   9.20791   10.82505   7.590764  
    1946   9.39915   11.03994   7.758363  
    1947   9.57699    11.2413    7.91269  
    1948   9.77549   11.47391   8.077066  
    1949   9.95869   11.67796   8.239429  
    1950   9.75781   11.44736    8.06827  
    1951   9.89388   11.58083   8.206924  
    1952   10.0456   11.74274   8.348381  
    1953   10.2032   11.91309   8.493413  
    1954   10.3687   12.09612   8.641381  
    1955   10.5568   12.29653   8.817131  
    1956   10.7342   12.49803   8.970412  
    1957   10.9142   12.70164   9.126683  
    1958   11.1232   12.93753   9.308931  
    1959    11.309   13.13925   9.478764  
    1960   11.4845   13.33012   9.638822  
    1961   11.5922   13.43556   9.748782  
    1962   11.7067     13.528   9.885418  
    1963   11.8256   13.63144   10.01974  
    1964   11.8886   13.67514   10.10202  
    1965   12.0371   13.81524   10.25896  
    1966   12.2032   13.98385   10.42257  
    1967   12.3684   14.14984   10.58695  
    1968   12.5691    14.3654   10.77287  
    1969   12.7946   14.61033   10.97881  
    1970   12.8122   14.63187   10.99245  
    1971   13.0332   14.86112    11.2053  
    1972   13.2322    15.0529   11.41142  
    1973   13.4385   15.25733   11.61971  
    1974   13.6062   15.41742   11.79493  
    1975   13.6418   15.45405   11.82953  
    1976   13.8092   15.62979    11.9886  
    1977   13.9829   15.80564    12.1602  
    1978   14.1207   15.93987    12.3015  
    1979   14.2632   16.08798   12.43836  
    1980    14.418   16.24842   12.58748  
    1981   14.5693   16.40734    12.7313  
    1982   14.6748   16.52073   12.82896  
    1983   14.6328   16.46975   12.79584  
    1984   14.7133   16.54206   12.88453  
    1985    14.811   16.63533   12.98666  
    1986   14.9052   16.72218   13.08813  
    1987   14.9839    16.7899    13.1778  
    1988   15.0808   16.88186   13.27975  
    1989   15.1671   16.96917   13.36512  
    1990   15.3737   17.20858   13.53892  
    1991   15.4795   17.37344   13.58556  
    1992   15.3935   17.21913   13.56783  
    1993   15.3784   17.18015   13.57662  
    1994   15.3825   17.17045   13.59451  
    1995   15.3821   17.15831   13.60592  
    1996   15.5399   17.34596   13.73392  
    1997   15.5321   17.32257   13.74154  
    1998   15.5106   17.29142    13.7297  
    1999   15.5474   17.32294   13.77185  
    2000   15.6309   17.41088   13.85098  
    2001   15.7313   17.52002   13.94259  
    2002   15.8909   17.69618   14.08568  
    2003   15.9988   17.81891   14.17861  
    2004   16.0932   17.92747   14.25899  
    2005   16.0844   17.92176   14.24706  
    2006   16.1415   17.97931   14.30367  
    2007   16.1612    18.0064   14.31596  
    2008   16.2468   18.10264    14.3909  
    2009   16.3935   18.28085   14.50623  
    2010   16.4698   18.36331   14.57631  
    2011   16.5673   18.47974    14.6548  
    2012   16.7482   18.70063   14.79577  
    2013   16.9165   18.88705   14.94587  
    2014      17.1    19.0932    15.1068  

. * Data were then loaded into MS Excel to produce figure
. 
. log close
      name:  <unnamed>
       log:  C:\Users\mbeissin\Desktop\Stata files for book\Logfiles\chapter6.log
  log type:  text
 closed on:  25 Jan 2022, 22:13:56
------------------------------------------------------------------------------------------------------------------
